perf: add HNN (2nd order AD) benchmarks

[skip ci]

perf/HNN/Project.toml

@@ -0,0 +1,15 @@
+[deps]
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
+Comonicon = "863f3e99-da2a-4334-8734-de3dacbe5542"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
+Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
+LuxCUDA = "d0bbae9a-e099-4d5b-a835-1c6931763bda"
+OrderedCollections = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+Reactant = "3c362404-f566-11ee-1572-e11a4b42c853"
+Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+
+[sources]
+Reactant = {path = "../.."}

perf/HNN/main.jl

@@ -0,0 +1,167 @@
+using Lux,
+    Random,
+    Reactant,
+    Enzyme,
+    Zygote,
+    BenchmarkTools,
+    LuxCUDA,
+    DataFrames,
+    OrderedCollections,
+    CSV,
+    Comonicon
+
+struct HamiltonianNN{E,M} <: AbstractLuxWrapperLayer{:model}
+    model::M
+
+    HamiltonianNN{E}(model::M) where {E,M} = new{E,M}(model)
+end
+
+function (hnn::HamiltonianNN{false})(x::AbstractArray, ps, st)
+    model = StatefulLuxLayer{true}(hnn.model, ps, st)
+    ∂x = only(Zygote.gradient(sum ∘ model, x))
+    n = size(x, ndims(x) - 1) ÷ 2
+    y = cat(
+        selectdim(∂x, ndims(∂x) - 1, (n + 1):(2n)),
+        selectdim(∂x, ndims(∂x) - 1, 1:n);
+        dims=Val(ndims(∂x) - 1),
+    )
+    return y, model.st
+end
+
+function (hnn::HamiltonianNN{true})(x::AbstractArray, ps, st)
+    ∂x = similar(x)
+    model = StatefulLuxLayer{true}(hnn.model, ps, st)
+    Enzyme.autodiff(Reverse, Const(sum ∘ model), Duplicated(x, ∂x))
+    n = size(x, ndims(x) - 1) ÷ 2
+    y = cat(
+        selectdim(∂x, ndims(∂x) - 1, (n + 1):(2n)),
+        selectdim(∂x, ndims(∂x) - 1, 1:n);
+        dims=Val(ndims(∂x) - 1),
+    )
+    return y, model.st
+end
+
+function loss_fn(model, ps, st, x, y)
+    pred, _ = model(x, ps, st)
+    return MSELoss()(pred, y)
+end
+
+function ∇zygote_loss_fn(model, ps, st, x, y)
+    _, dps, _, dx, _ = Zygote.gradient(loss_fn, model, ps, st, x, y)
+    return dps, dx
+end
+
+function ∇enzyme_loss_fn(model, ps, st, x, y)
+    _, dps, _, dx, _ = Enzyme.gradient(
+        Reverse, loss_fn, Const(model), ps, Const(st), x, Const(y)
+    )
+    return dps, dx
+end
+
+function reclaim_fn(backend, reactant)
+    if backend == "gpu" && !reactant
+        CUDA.reclaim()
+    end
+    GC.gc(true)
+    return nothing
+end
+
+Comonicon.@main function main(; backend::String="gpu")
+    @assert backend in ("cpu", "gpu")
+
+    Reactant.set_default_backend(backend)
+    filename = joinpath(@__DIR__, "results_$(backend).csv")
+
+    @info "Using backend" backend
+
+    cdev = cpu_device()
+    gdev = backend == "gpu" ? gpu_device(; force=true) : cdev
+    xdev = reactant_device(; force=true)
+
+    df = DataFrame(
+        OrderedDict(
+            "Kind" => [],
+            "Fwd Vanilla" => [],
+            "Fwd Reactant" => [],
+            "Fwd Reactant SpeedUp" => [],
+            "Bwd Zygote" => [],
+            "Bwd Reactant" => [],
+            "Bwd Reactant SpeedUp" => [],
+        ),
+    )
+
+    mlp = Chain(
+        Dense(32, 128, gelu),
+        Dense(128, 128, gelu),
+        Dense(128, 128, gelu),
+        Dense(128, 128, gelu),
+        Dense(128, 1),
+    )
+
+    model_enz = HamiltonianNN{true}(mlp)
+    model_zyg = HamiltonianNN{false}(mlp)
+
+    ps, st = Lux.setup(Random.default_rng(), model_enz)
+
+    x = randn(Float32, 32, 1024)
+    y = randn(Float32, 32, 1024)
+
+    x_gdev = gdev(x)
+    y_gdev = gdev(y)
+    x_xdev = xdev(x)
+    y_xdev = xdev(y)
+
+    ps_gdev, st_gdev = gdev((ps, st))
+    ps_xdev, st_xdev = xdev((ps, st))
+
+    @info "Compiling Forward Functions"
+    lfn_compiled = @compile sync = true loss_fn(model_enz, ps_xdev, st_xdev, x_xdev, y_xdev)
+
+    @info "Running Forward Benchmarks"
+
+    t_gdev = @belapsed CUDA.@sync(loss_fn($model_zyg, $ps_gdev, $st_gdev, $x_gdev, $y_gdev)) setup = (reclaim_fn(
+        $backend, false
+    ))
+
+    t_xdev = @belapsed $lfn_compiled($model_enz, $ps_xdev, $st_xdev, $x_xdev, $y_xdev) setup = (reclaim_fn(
+        $backend, true
+    ))
+
+    @info "Forward Benchmarks" t_gdev t_xdev
+
+    @info "Compiling Backward Functions"
+    grad_fn_compiled = @compile sync = true ∇enzyme_loss_fn(
+        model_enz, ps_xdev, st_xdev, x_xdev, y_xdev
+    )
+
+    @info "Running Backward Benchmarks"
+
+    t_rev_gdev = @belapsed CUDA.@sync(
+        ∇zygote_loss_fn($model_zyg, $ps_gdev, $st_gdev, $x_gdev, $y_gdev)
+    ) setup = (reclaim_fn($backend, false))
+
+    t_rev_xdev = @belapsed $grad_fn_compiled(
+        $model_enz, $ps_xdev, $st_xdev, $x_xdev, $y_xdev
+    ) setup = (reclaim_fn($backend, true))
+
+    @info "Backward Benchmarks" t_rev_gdev t_rev_xdev
+
+    push!(
+        df,
+        [
+            "HNN",
+            t_gdev,
+            t_xdev,
+            t_gdev / t_xdev,
+            t_rev_gdev,
+            t_rev_xdev,
+            t_rev_gdev / t_rev_xdev,
+        ],
+    )
+
+    display(df)
+    CSV.write(filename, df)
+
+    @info "Results saved to $filename"
+    return nothing
+end

perf/HNN/results_cpu.csv

@@ -0,0 +1,2 @@
+Kind,Fwd Vanilla,Fwd Reactant,Fwd Reactant SpeedUp,Bwd Zygote,Bwd Reactant,Bwd Reactant SpeedUp
+HNN,0.012209751,0.002101077,5.811186834180757,0.173089096,0.004597676,37.64708430955117

perf/HNN/results_gpu.csv

@@ -0,0 +1,2 @@
+Kind,Fwd Vanilla,Fwd Reactant,Fwd Reactant SpeedUp,Bwd Zygote,Bwd Reactant,Bwd Reactant SpeedUp
+HNN,0.000681027,8.4721e-5,8.038467440186022,0.003330234,0.00012123,27.470378619153674

perf/KAN/main.jl

@@ -35,6 +35,7 @@ function reclaim_fn(backend, reactant)
         CUDA.reclaim()
     end
     GC.gc(true)
+    return nothing
 end
 
 Comonicon.@main function main(; backend::String="gpu")